This paper argues that generative AI can degrade research by eroding the practices through which scholarly judgment is formed and academic trust is built. The practices that constitute the production and validation of knowledge cannot be reduced to the final outputs of research, which is what AI effectively simulates. Consequently, when researchers delegate central inquiry tasks to systems like Large Language Models, they may cease to enact these practices and, with them, lose access to the formation they provide.
An individual research output generated by AI may appear improved, but the researcher behind it fails to develop.
To counter this risk, merely keeping humans in the loop as prompters or quality checkers of AI outputs is insufficient. Instead, what is needed is a renewed commitment to research as a lived practice, where judgment is formed gradually, often through frictions and participation in a scholarly community.
We defend this practice because it rests on four sources and warrants of research that cannot be automated: tacit knowledge, personal commitment, socialization, and deep reading. This practice enacts what we call second scholarship, which we understand as the reappropriation of scholarly craft, chosen out of a critical experience of what generative AI can and cannot do. What cannot and should not be delegated becomes what research communities must value and answer for. This is what is left for us.
Blogger's Review: This paper profoundly reveals the potential threats of AI in academic research, emphasizing the importance of human involvement. Only by reclaiming academic practices can we maintain the depth and breadth of knowledge, ensuring that the true value of research is not replaced by AI. Researchers need to realize that the use of AI must be predicated on fostering personal growth and participation in the academic community.